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Proceedings Paper

Robust concrete crack recognition based on improved image segmentation and machine learning
Author(s): Qiancheng Zhao; Jiang Shao; Tianlong Yang
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Paper Abstract

This paper presents an automatical crack recognition approach. Compared with the existing methods, it has a significant increase in robustness and efficiency when faced with widely varying field conditions. Inherent characteristics of crack images are exploited using proportional segmentation, combined with robust feature extraction to improve machine learning classifier performance. Experiments show that this method perform well in crack images recognition across different concrete conditions.

Paper Details

Date Published: 7 March 2019
PDF: 7 pages
Proc. SPIE 11053, Tenth International Symposium on Precision Engineering Measurements and Instrumentation, 110531K (7 March 2019); doi: 10.1117/12.2511359
Show Author Affiliations
Qiancheng Zhao, Hunan Univ. of Science and Technology (China)
Jiang Shao, Hunan Univ. of Science and Technology (China)
Tianlong Yang, Hunan Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11053:
Tenth International Symposium on Precision Engineering Measurements and Instrumentation
Jiubin Tan; Jie Lin, Editor(s)

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